|
|
 |
|
| Thesis topic proposal |
| |
Description of the research topic:
1/ Quality control is outstanding area of production management. The effectiveness of the applied quality control methods strongly depends on the performance of the measurement system. Control charts are widely used tools not only in statistical process control (SPC), but nowadays also in project and maintenance management. Most of the control charts disregard the arising risks during the control procedure. Nevertheless, the monitored characteristics can be distorted by different sources like measurement error in production and maintenance management and estimation uncertainty in the field of project management.
2/ Ignoring measurement or estimation uncertainty can increase the cost of decisions. The control charts applications in project and maintenance management are built on the similarities of the process and the project management, however, the time-dependent nature of the maintenance and project scheduling processes are still ignored. In this research, new class of risk-based control charts shall be developed to track and control both the maintenance scheduling processes and the project implementations to reduce the decision costs.
3/Conformity- and process control are important fields of operation management. Although,
traditional control chart design does not take the effect of measurement errors into account, producers' and suppliers' risks are important topics in conformity or process control (Lira, 1999, Giri 2011).
Accuracy of measurement system or process is outstandingly important. Measurement uncertainty can lead to incorrect decisions like unnecessary manufacture stoppage or missed intervention (Pendrill, 2008). Therefore, the rate of producer's and customer's risk is strongly impacted by the performance of the measurement process (Peruchi et. al., 2013}).
Several researchers showed that measurement uncertainty needs to be considered in conformity testing (see, e.g. Forbes, 2006, Pendrill, 2008, Kosztyán et. al. 2017), and statistical process control (Kanazuka, 1986, Mittag & Stemann, 1998, Tang et, al. 2018)
Not only the effect of measurement errors on conformity/process control performance was examined but solutions were designed in order to decrease the number of incorrect decisions. Kosztyán et al. (2017) presented risk-based treatment of measurement uncertainty in conformity control and the same risk-based aspect was applied to the field of statistical control charts (Kosztyán-Katona 2016,2018). The proposed method proved itself to be effective in reduction of decision risks however, it was tested only in case of controlling production processes, but there is no risk-based control charts for controlling projects and the maintenance processes yet.
In the case of maintenance processes, the observed values are not stable in time, usually not normally distributed and not from stationery processes (Papic & Pantelic, 2014); therefore, traditional control charts cannot be used. Nevertheless, the application of risk-based control charts in maintenance promises a more effective early warning method.
Although there are several EVA (earned value analysis) based approaches (see, e.g. Leu & Lin, 2008) for project tracking and control, Colin and Vanhoucke (2015) showed, that using only the EVA measures is not sufficient for reaching a high detection rate while keeping the false positive rate of signals low. To improve the performance of the project control chart additional information (e.g. task criticality indexes, schedule risk analysis) is used besides the EVA (Vanhoucke, 2019) or multivariate charts are applied (Hadian & Rahimifard, 2019). The results of these approaches are surpassable by taking the estimation uncertainty, the decision consequences and the actual logic/dependency structure of the project into account.
4/ To develop a new risk-based control chart family to improve early warning systems, machine utilization or reduce operation costs in maintenance.
To propose risk-based control charts for project controls to decrease the decision costs and increase the effects of controls interventions.
5/ Kosztyán, Z. T. (2015). Exact algorithm for matrix-based project planning problems. Expert Systems with Applications, 42(9), 4460-4473.
Kosztyán, Zs.T. Katona, A.I.(2016), Risk-based multivariate control chart,
Expert Systems with Applications, Volume 62,pp. 250-262.
Kosztyán, Zs.T., Hegedűs, Cs. & Katona, A. (2017) Treating measurement uncertainty in industrial conformity control. Central European Journal of Operation Research 25: 907.
Kosztyán, Zs. T., & Katona, A. I. (2018). Risk-Based X-bar chart with variable sample size and sampling interval. Computers & Industrial Engineering, 120, 308-319.
Kosztyán, Zs. T. (2018). Serviceability of large-Scale systems. Simulation Modelling Practice and Theory, 84, 222-231.
6/ Omega (D1 - Management Science and Operations Research, A)
Quality and Reliability Engineering International (Q1 – Management Science and Operations Research, A)
Computers and Operations Research (D1 - Management Science and Operations Research, A)
International Journal of Project Management (D1 – Management of Technology and Innovation, D1 - Business and International management, A)
Computers & Industrial Engineering (D1, D)
REFERENCES:
Colin, J., Vanhoucke, M.(2015), Developing a framework for statistical process control approaches in project management, International Journal of Project Management, 33(6), pp. 1289-1300, https://doi.org/10.1016/j.ijproman.2015.03.014.
Forbes, A. B. (2006). Measurement uncertainty and optimized conformance assessment. Measurement, 39(9), 808-814.
Giri, B. C. (2011). Managing inventory with two suppliers under yield uncertainty and risk aversion. International Journal of Production Economics, 133(1), 80-85.
Hadian, H., Rahimifard, A. (2019), Multivariate statistical control chart and process capability indices for simultaneous monitoring of project duration and cost, Computers & Industrial Engineering,
Kanazuka, T. (1986). The effect of measurement error on the power of X-R charts. Journal of Quality Technology, 18(2), 91-95.
Kosztyán, Zs.T., Hegedűs, Cs. & Katona, A. (2017) Treating measurement uncertainty in industrial conformity control. Central European Journal of Operation Research 25: 907.
Kosztyán, Zs.T. Katona, A.I.(2016), Risk-based multivariate control chart,
Expert Systems with Applications, Volume 62,pp. 250-262.
Kosztyán, Z. T., & Katona, A. I. (2018). Risk-Based X-bar chart with variable sample size and sampling interval. Computers & Industrial Engineering, 120, 308-319.
Leu, S.S., Lin, Y.C., (2008). Project performance evaluation based on statistical process control techniques. J. Constr. Eng. Manag. 134, pp. 813–819.
Lira, I. (1999). A Bayesian approach to the consumer's and producer's risks in measurement. Metrologia, 36(5), 397.
Mittag, H. J., & Stemann, D. (1998). Gauge imprecision effect on the performance of the XS control chart. Journal of Applied Statistics, 25(3), 307-317.
Papic, L. & Pantelic (2014), Maintenance-oriented safety control charts: International Journal of System Assurance Engineering and Management 5: pp. 149-154
Pendrill, L. R. (2008). Operating ‘cost’characteristics in sampling by variable and attribute. Accreditation and quality assurance, 13(11), 619-631
Peruchi, R. S., Balestrassi, P. P., de Paiva, A. P., Ferreira, J. R., & de Santana Carmelossi, M. (2013). A new multivariate gage R&R method for correlated characteristics. International Journal of Production Economics, 144(1), 301-315.
Tang, A., Castagliola, P., Sun, J., & Hu, X. (2018). The effect of measurement errors on the adaptive EWMA chart. Quality and Reliability Engineering International, 34(4), 609-630
Vanhoucke, M. (2019), Tolerance limits for project control: An overview of different approaches, Computers & Industrial Engineering 127, pp. 467-479.
Required language skills: English
Number of students who can be accepted: 1
Deadline for application: 2023-01-31 |
|
|
|
|
|
|
|
Login
